**5.2 Set up of a CRS using optical touch screen devices**

With the above needs in mind, we applied the optical touch screen to the development of the next generation CRS, or Z-Writers. The major part of the Z-Writer is a clipboard style writing surface. Students can put a regular notepad or a stack of test paper on it. They can write on the paper just like what they are doing in the regular test. The difference of the Z-Writer from the regular clipboard is that a sensor bar is installed at the top of the pad. The sensor bar can take either the PLUS or PLUM configuration as shwon in Figure 7A or Figure 7B.

Fig. 7. Hardware setup.

Like tablet PCs or xPads, the Z-Writer can be used by individual users. That is, each Z-Writer is associated with a computer via Bluetooth wireless connection. Since only the movement of the pen tips will be recorded and transmitted, no high capacity data transmission is required. Off-the-shelf mainstream Bluetooth 2.1 transmitters are capable of sending and receiving data without delay.

We can also network Z-Writers in a CRS setting. Each Bluetooth master can only handle up to 7 devices with today's technology. In Linux set up, we can simply add extra Bluetooth dongles to increase the number of devices the computer can handle. For Windows or Mac OS, the operating systems can not handle more than one Bluetooth master. Therefore, we developed a special Scatternet (Mockel et al, 2007) to expand the capacity of the Z-Writers.

As seen in Figure 8, we divide all the Z-Writers in a classroom to groups of 6. One of them equips a sub-server with an enhanced antenna. It acts as both a receiver and a relay. It collects the data from the group and relay them to the Bluetooth master in the host computer. In this network, each computer can handle up to 7 groups or 42 Z-Writers. That is enough for most classes and more than the maximum capacity that a teacher can handle at the individual level without a teaching assistant. For the larger classes with the help of teaching assistants, a local network of multiple computers or an outright change to using 802.11 can be employed. However, these are beyond the topic of this discussion.

Fig. 8. Set up of a Classroom Response System.

128 Virtual Reality and Environments

operating system in one classroom unless they are provided for free. Third is the distraction of the other software, especially the communication and entertainment software installed in the tablets. When a student is having an electronic device in hand, it is not easy to resist the temptation of returning a quick text message or playing a small game while the teacher is

With the above needs in mind, we applied the optical touch screen to the development of the next generation CRS, or Z-Writers. The major part of the Z-Writer is a clipboard style writing surface. Students can put a regular notepad or a stack of test paper on it. They can write on the paper just like what they are doing in the regular test. The difference of the Z-Writer from the regular clipboard is that a sensor bar is installed at the top of the pad. The sensor bar can take either the PLUS or PLUM configuration as shwon in Figure 7A or

Like tablet PCs or xPads, the Z-Writer can be used by individual users. That is, each Z-Writer is associated with a computer via Bluetooth wireless connection. Since only the movement of the pen tips will be recorded and transmitted, no high capacity data transmission is required. Off-the-shelf mainstream Bluetooth 2.1 transmitters are capable of

We can also network Z-Writers in a CRS setting. Each Bluetooth master can only handle up to 7 devices with today's technology. In Linux set up, we can simply add extra Bluetooth dongles to increase the number of devices the computer can handle. For Windows or Mac OS, the operating systems can not handle more than one Bluetooth master. Therefore, we developed a special Scatternet (Mockel et al, 2007) to expand the capacity of the Z-Writers.

**5.2 Set up of a CRS using optical touch screen devices**

not around.

Figure 7B.

Fig. 7. Hardware setup.

sending and receiving data without delay.

The host computer on the teacher's side collects all the data from the sub-servers and save them into a database. A preview program converts the data into pixel points and display them in a preview window like the left part of Figure 9. With a quick glance, the teachers can have an instant view of how the students are performing. When the teachers need to annotate an individual student's work, they can select and magnify the one of interest and display it as seen in the right half of Figure 9 for in-class review and analysis.

Fig. 9. Display in teacher's computer screen. Left: Real-time preview with all inputs. Right: Magnified view of individual answers.

Optical Touch Screen and Its Application as a Next Generation Classroom Response System 131

4. **3D input**. As mentioned earlier in this article, the action space and virtual force can be

5. **Multi-touch**. Existing computer software can easily detect and follow multiple image features. It will be easy to include multi-touch functions. Combined with virtual force,

6. **Compact and light weight**. Comparing to the existing optical touch screen using behind screen or user side camera, this side camera method is compact and can save precious space. The cameras are generally small and will not increase the weight or size

1. **Real-time feedback.** All educators are facing the perpetual challenge of assessing how well the students understand each course, each example, or even each sentence. Homework, quizzes and exams evaluate each individuals but with latency. Group discussion, flash cards, and the original Classroom Response Systems (Clickers) provide instant but collective feedbacks. The Z-Writer Group combines the benefits of the two

2. **Progress monitor.** From one computer display, teachers can monitor not only the answers but how the students proceed from start to finish at both collective and

3. **After-class evaluation.** Teachers can replay the writings of every answer after the class. Therefore, they can analyze each student and address their specific needs with higher

4. **Open-ended questions.** Many in-class assessment tools rely on multi-choice questions, which are not enough for STEAM subjects. The Z-Writer Group will enable teachers to give open-ended questions in classes and obtain real-time feedbacks. This will greatly

Currently we are working on improving the stability and noise reduction on the optical touch screen algorithm and its use on classroom response system described in this paper. We are also working on expanding the applications of the optical touch screen to art and calligraphy field for paperless painting with realistic strokes. We are also exploring the use

Albinsson, P & Zhai, S. (2003). High Precision Touch Screen Interaction, *Proceedings of the*

*SIGCHI conference on Human factors in computing systems*, ISBN:1-58113-630-7, Fort

used to achieve the effect of 3D input and to obtain virtual force feedback.

the multi-touch function can make inputting more versatile and powerful.

of the computer display like many existing touch screens.

by providing instant individual feedbacks to the teachers.

encourage creativities from the students.

of 802.11 for the auditorium size CRS settings.

Lauderdale, FL, April 5-10, 2003

**6.2 Benefit of the next generation CRS using touch screen writers**

height-width ratios.

individual levels.

accuracy.

**6.3 Future work**

**7. References**

viewable area quadruples, so is the material used and the computing power needed. In contrast, in the methods introduced in this article, the same two cameras or camera and mirror combination can be easily scaled up or down to screens with different sizes and

Figure 10 is a sample course material common to Physics or Engineering majors. One main topic of such majors is load analysis with Free Body Diagrams (FBD). Figure 10 gives a typical example of the force analysis. On left is a block resting on a slanted surface. As shown on right, students need to set up a proper coordinate system, draw the forces, and then write down the governing equations on each direction to find out the magnitudes of the forces.

Before the class, the teacher prepares questions like the one shown in the left part of Figure 10 and makes copies on regular paper. In the class time, the teacher first explains the concepts of FBD and provides examples. Then the handouts with the questions will distributed to the class. The students put the handouts on the writing surfaces of the Z-Writers and then write their answers on the paper just like any other classes. The Z-Writers transfer the results to the host computer for the teacher to review in real time. The teacher is then able to adjust the next step accordingly, such as revisiting the subject, moving on to the next topic, or giving annotations to selected answers. At the end of the class, the students will carry their own paper home while the teacher keeps a soft copy of all answers for future reference.

Fig. 10. A typical example of load and motion analysis. Left: Given by teacher. Right: Student answer.


2. Ferromagnetic core

Magnetic fields are the essential means by which energy is converted from one form to another in motors, generators, and transformers. The most important class of the magnetic materials is the ferromagnetic materials such as iron, cobalt, nickel, and manganese [2].

There are four basic principles which describe how magnetic fields are used [2]:

#### 2.1. The magnetic field

The magnetic field is produced by induced current in Ampere's law:

$$
\oint \mathbf{H}.\mathbf{dl} = I\_{\text{net}} \tag{1}
$$

where Inet produces magnetic field intensity and H and dl are the length integration along a path. If the core is produced from ferromagnetic material (Figure 1), then all the magnetic field produced within the core will remain inside the core. Therefore, the path of integration dl in the Ampere's law is the mean path length lc [2].

The current passing in the path of the integration Inet is NI since the coil of the wire divides the path of integration into N times when the current passes through it:

$$H.l = NI \Rightarrow H = \frac{NI}{l} \tag{2}$$

The magnetic field intensity H is the effort in which a current is applying to establishment of a magnetic field. Strength of the magnetic field depends on the material of core. There is a relationship between the magnetic field intensity, the material magnetic permeability µ, and the magnetic flux produced within the material as shown in Eq. (3):

$$B = \mu H \tag{3}$$

The permeability of free space is called µ0 and equal to 4π � 10�7 H/m, and the relative permeability is the permeability of any other material compared to the free space permeability:

$$
\mu\_r = \frac{\mu}{\mu\_0} \tag{4}
$$

In the core (Figure 1), the magnitude of the flux density is given by

$$B = \mu H = \mu \frac{NI}{l} \tag{5}$$

Therefore, the total flux in a given area is expressed in Eq. (6). This equation reduced if the flux density vector is perpendicular to any plane of area, and if the flux density is constant throughout the area, then to

$$
\oint\_A \phi = B.dA \Rightarrow \phi = B.A = \mu HA = \mu \frac{NI}{l} A \tag{6}
$$

Figure 1. Ferromagnetic core.

The study of an efficient power system starts with understanding the behavior of each component that develops this system. Electric machines used in power systems (generators, motors, and transformers) will be examined through analytical expressions and computer simulation. The importance of simulation is that these components could be studied before it is manufactured; thus, the consequences of changing dimensions and parameters can be assessed.

This simulation will be implemented in an educational tool, going from the basic operation principles, through developing models and equations toward the solution. The graphical user interface of MATLAB allows the students to study and analyze the effect of each parameter in

This chapter will discuss the implementation of ferromagnetic core using graphical user interface taking into consideration the effects of air gap and fringing of a ferromagnetic core. Then, a detailed study of output power and losses with voltage regulation and efficiency of a single- and three-phase transformer will be established. In addition, a special survey will be accomplished concerning the types of DC motors and generators. Finally, this chapter will be concluded by providing an adequate research on the induction machines including their

This chapter presents learning situations going from the theoretical expansion to the graphical

Magnetic fields are the essential means by which energy is converted from one form to another in motors, generators, and transformers. The most important class of the magnetic materials is

1. A wire produces a magnetic field in the area around it when current passes through it.

2. A change in magnetic field, by mutual inductance, induces a voltage in the coil of wire:

3. In the presence of a magnetic field, a current-carrying wire has a force induced on it: this is

4. In the presence of a magnetic field, a moving wire has a voltage induced in it: this is the

∮ H:dl ¼ Inet ð1Þ

order to understand its electric behavior with respect to its electric model.

parametric study, and it will be achieved by a general conclusion of this work.

interpretation. It is a teaching methodology toward the science education.

the ferromagnetic materials such as iron, cobalt, nickel, and manganese [2].

The magnetic field is produced by induced current in Ampere's law:

this is the principle of transformer action.

the principle of motor action.

principle of generator action.

2.1. The magnetic field

There are four basic principles which describe how magnetic fields are used [2]:

2. Ferromagnetic core

154 Science Education - Research and New Technologies

#### 2.2. Magnetic circuits

Magnetic flux is produced when the current in a coil of wire is wrapped around a core. This is similar to a voltage in an electric circuit producing a current flow. Thus, a "magnetic circuit" is defined by equations that are similar to that of an electric circuit. In the design of electric machines and transformers, the magnetic circuit model is used to simplify the complex design process [2].

The voltage or electromotive force drives the current flow in the electric circuit. The magnetomotive force of the magnetic circuit is denoted by where is the magnetomotive force in ampere-turns. In the magnetic circuit, the applied magnetomotive force causes flux (φ) to be produced (Figure 2).

The relationship that governs the magnetomotive force and flux is given by

$$\mathfrak{S} = NI = \phi \mathfrak{R} \tag{7}$$

The permeance of a magnetic circuit is the reciprocal of its reluctance. Therefore, the relation between magnetomotive force and flux can be expressed as

$$
\phi = \mathfrak{S}P \Rightarrow \phi = \mathfrak{F}\frac{1}{\mathfrak{R}} \tag{8}
$$

It is easier to work with the permeance of a magnetic field than with its reluctance.

The resulting flux and reluctance of a core are shown in Eqs. (9) and (10), respectively:

$$\phi = \mathfrak{F}\frac{\mu A}{l} \tag{9}$$

$$\mathcal{R} = \frac{l}{\mu A} \tag{10}$$

The equivalent reluctance of a number of reluctances in series is just the sum of the individual reluctances:

Figure 2. (a) A simple electric circuit. (b) The magnetic circuit analogue to a transformer core.

Electric Machines: Tool in MATLAB http://dx.doi.org/10.5772/intechopen.68957 157

$$\mathcal{R}\_{eq} = \mathcal{R}\_1 + \mathcal{R}\_2 + \mathcal{R}\_3 + \dots \tag{11}$$

The equivalent reluctance of a number of reluctances in parallel is just the sum of the individual reluctances:

$$\mathcal{R}\_{eq} = \frac{1}{\mathcal{R}\_1} + \frac{1}{\mathcal{R}\_2} + \frac{1}{\mathcal{R}\_3} + \dots \tag{12}$$

The reluctance of each leg of a ferromagnetic core is

$$\mathcal{R}\_{\mathbf{x}} = \frac{l\_{\mathbf{x}}}{\mu\_r \mu\_0 A\_{\mathbf{x}}} \mathbf{A} . t \!/\_{wb} \tag{13}$$

The air-gap reluctance at leg X is

2.2. Magnetic circuits

156 Science Education - Research and New Technologies

produced (Figure 2).

process [2].

reluctances:

Magnetic flux is produced when the current in a coil of wire is wrapped around a core. This is similar to a voltage in an electric circuit producing a current flow. Thus, a "magnetic circuit" is defined by equations that are similar to that of an electric circuit. In the design of electric machines and transformers, the magnetic circuit model is used to simplify the complex design

The voltage or electromotive force drives the current flow in the electric circuit. The magnetomotive force of the magnetic circuit is denoted by where is the magnetomotive force in ampere-turns. In the magnetic circuit, the applied magnetomotive force causes flux (φ) to be

The permeance of a magnetic circuit is the reciprocal of its reluctance. Therefore, the relation

φ ¼ ℑP ) φ ¼ ℑ 1

φ ¼ ℑ μA

ℜ ¼ l

The equivalent reluctance of a number of reluctances in series is just the sum of the individual

It is easier to work with the permeance of a magnetic field than with its reluctance.

Figure 2. (a) A simple electric circuit. (b) The magnetic circuit analogue to a transformer core.

The resulting flux and reluctance of a core are shown in Eqs. (9) and (10), respectively:

ℑ ¼ NI ¼ φℜ ð7Þ

ℜ ð8Þ

l ð9Þ

μA ð10Þ

The relationship that governs the magnetomotive force and flux is given by

between magnetomotive force and flux can be expressed as

$$\mathcal{R}\_{\text{xa}} = \frac{l\_{\text{xa}}}{\mu\_0 A\_{\text{xa}}} A.t/\_{\text{wb}} \tag{14}$$

The total flux of the ferromagnetic core is

$$
\phi\_{TOT} = \frac{\mathfrak{F}}{\mathfrak{R}\_{eq}} wb \tag{15}
$$

#### 2.3. Implement in MATLAB GUI

When implementing in MATLAB, the user will add certain input which will then be calculated, and the result will be displayed. Below is a block diagram of the system.

The user fills the number of regions with availability of air gap indicating which leg is available and the details for core type such as relative permeability of the material and number of turns with the current (Figure 3). The results of the calculated parameters such as total flux and total reluctance and magnetomotive force of ferromagnetic core are displayed (Figure 4).

Figure 3. Ferromagnetic core GUI block diagram.

Figure 4. Graphical user interface for ferromagnetic core.

Also, the user should add the parameters of the ferromagnetic core such as length, area, air gap, and fringing percentage of each leg of the core; the ferromagnetic core is displayed after entering the inputs. Push buttons are added to load, save data, clear, and quit.


Substantial similarity between morphological effects with derived and compound words also emerges when considering morpho-orthographic segmentation. The vast majority of this literature has investigated derived and pseudo-derived words (see above), but in a recent paper Fiorentino and Fund-Reznicek (2009) reported significant and equivalent masked priming effects for transparent (teacup-TEA) and opaque compounds (honeymoon-HONEY, carpet-CAR), as compared to orthographic, non-morphological controls (penguin-PEN). The effect held for both initial and final constituent word priming (flagpole-FLAG vs. classroom-ROOM), and clearly mirrors what has been reported for derived words, thus suggesting that the early morpho-orthographic segmentation proposed byRastle et al. (2004) generalizes to all types of morphologically complex words.

Perhaps even more strikingly, data gathered on inflected and compound words are closely similar for what concerns the rejection time of morphologically structured non-words in

lexical decision tasks. In fact, it has been documented, for both pseudo-inflected and pseudo-compound non-words, that nonwords made up entirely by non-existing morphemes (e.g., "iblish" and "thrimnade") or by a non-existing first element and an existing second morpheme (e.g., "ibvive" and "flurbpair") are easier to reject than non-words made up of a real morpheme as a first element followed by a non-existing second element (e.g.,"inlish" and "spellcung"). In turns, these latter non-words are easier to judge than non-words entirely made up of real morphemes (e.g.,"invive" and "toastpull"; see Taft and Forster, 1975; Lima and Pollatsek, 1983; Taft et al., 1986; and Monsell, 1985).

Clearly, this evidence is far from suggesting that the visual identification system processes inflected, derived and compound words in exactly the same way. However, it does suggest that at least some (peripheral) processing steps are common to all types of complex words and, more generally, that there should be a more tight integration between the literature on inflected, derived and compound words.

# **THE TARGET LIST**

In this paper we reviewed the behavioral literature on the visual identification of complex words with the aim of building a list of established facts that might help in adjudicating between existing theories, and eventually in developing a comprehensive computational model of how complex words get identified by the visual system.

The list should include these effects:

In unmasked priming:

In masked priming:

From a theoretical point of view, it is not easy to see in a glimpse whether these effects speak clearly against or in favor of any existing theory. Surely, morphological effects in non-words exclude the possibility that morphological information only comes into play after lexical identification. For what concerns the other big dichotomies illustrated at the beginning of the paper (e.g., one vs. dual-route models; PDP vs. localist models), there is no clear indication popping out. This is exactly where computational modeling comes as a useful tool; in fact, by implementing theories in a computer program it becomes easier to understand unequivocally which model survives confrontation with the data (in particular for what concerns the simulation of several effects with the same system settings), and which does not.

Obviously, this list is by no means definitive (new evidence is continuously arising on what seems to be a hotly debated topic), nor necessarily complete. We made all our efforts to ensure that we covered all the relevant data, but with such a huge amount of evidence amassed over the last 40 years, it is possible that we have missed some important results. We encourage anyone to flag possible gaps, also taking advantage of the brilliant "Comment" tool made available upon the open-access policy adopted by this Journal.

The main point that we want to make with this paper, however, is not about the list *per se*; rather, we hope that having a list of benchmark effects will help the field to move forward in a more cumulative and cooperative fashion. In the spirit of the nested modeling principle put forward more than a decade ago in the related field of reading aloud (Grainger and Jacobs, 1996), we hope that in the near future (i) existing models will confront on the basis of their ability to account for these (or other) benchmark effects; (ii) credit and blame will be assigned to specific parts of each theory for their successes and failures in this attempt; (iii) in proposing any new theory, substantial effort will be spent in explaining how the new theory relates with its predecessors, how it extends them, why it does that in the way that it does, which new effects it is able to explain that its predecessors were not able to explain, and which effects it is still not able to explain that were also outside the grasp of its predecessors.

# **ACKNOWLEDGMENTS**

This work wasfunded by a"FIRB-Futuro in Ricerca"grant awarded to Davide Crepaldi by the Italian Ministry of Education, University and Research (RBFR085K98). Authors' contributions are as follows: Davide conceived the paper; Simona conducted the literature search, critically revised the results, and drafted the paper; Simona and Davide revised the paper.

# **REFERENCES**

indicate morphological decomposition in visual word recognition. *Neurosci. Lett.* 318, 149–152.

*J. Verb. Learn. Verb. Behav.* 22, 310–332.

perspective on frequency and family size in Dutch and Hebrew. *J. Mem. Lang.* 53, 496–512.

in the processing of Finnish compound words. *Lang. Cogn. Process* 20, 261–290.

Chinese characters recognition. *J. Mem. Lang.* 40, 498–519.

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 24 February 2012; accepted: 19 June 2012; published online: 12 July 2012. Citation: Amenta S and Crepaldi D (2012) Morphological processing as we know it: an analytical review of morphological effects in visual word identification. Front. Psychology 3:232. doi: 10.3389/fpsyg.2012.00232*

*This article was submitted to Frontiers in Language Sciences, a specialty of Frontiers in Psychology.*

*Copyright © 2012 Amenta and Crepaldi. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.*

# Future morphology? Summary of visual word identification effects draws attention to necessary efforts in understanding morphological processing

# *Dirk Koester 1,2\**

*1 Neurocognition and Action – Biomechanics Research Group, Faculty of Psychology and Sport Science, Bielefeld University, Bielefeld, Germany*

*2 Center of Excellence – Cognitive Interaction Technology, Bielefeld University, Bielefeld, Germany*

*\*Correspondence<EMAIL_ADDRESS>
#### *Edited by:*

*Carlo Semenza, Università degli Studi di Padova, Italy*

#### *Reviewed by:*

*Carlo Semenza, Università degli Studi di Padova, Italy Marco Marelli, University of Trento, Italy*

#### **A commentary on**

# **Morphological processing as we know it: an analytical review of morphological effects in visual word identification**

*by Amenta, S., and Crepaldi, D. (2012). Front. Psychol. 3:232. doi: 10.3389/fpsyg.2012.00232*

This commentary discusses the insights and suggestions of a review by Amenta and Crepaldi (2012) in *Frontiers in Psychology*. The authors have diagnosed a controversial accumulation of findings in the field of visual word identification and, hence, have provided an overview of the field with the aim to separate substantial effects from findings that need further confirmation. The authors aim to provide a broad basis for theory development. Amenta and Crepaldi (2012) are the first to attempt a comprehensive psycholinguistic review of the major forms of word formation, namely inflection, derivation, and compounding. The authors summarize 17 robust experimental effects and suggest that "any theory should be able to explain" this set of experimental effects (cf. abstract). Thus, the listed effects are supposed to help to decide which among competing theories have more explanatory power and might thus be considered scientifically superior.

Without repeating all effects, Amenta and Crepaldi propose that stem frequency, family size, word entropy, and the number of affix allomorphs are main determinants in visual word identification. Furthermore, non-word processing is suggested to be relevant for morphological theories, if the non-words are made from morphemes. Other relevant properties are proposed for methodologically specified situations. For example, if stimuli are fully visible, morphological priming effects are only proposed for semantically related words, and inflectional priming yields greater effects than derivational priming. In contrast, in masked priming, morphological effects are comparable in magnitude for semantically transparent and opaque words. Also, inflectional and derivational priming is suggested to yield comparable effect sizes.

Amenta and Crepaldi's review point toward relevant linguistic (e.g., productivity) and psycholinguistic variables (e.g., frequency measures) and their relations regarding visual word identification. Such knowledge will guide future investigations and, hence, impact also models of language performance. The authors suggest that these findings provide a basis for the evaluation of competing theories and, in doing so, to contribute to future theory development; in their own words, to construct an "allinclusive model of visual identification of morphologically complex words." In light of the specificity of the insights, these broad suggestions leave the reader with the impression of a gap between insights and suggestions. The authors deal with a specific functional step (visual word identification) of the more complex human ability of (single word) reading and it is not necessary that the relevant variables for identification can be extrapolated to other functional steps (e.g., morphosyntactic and/or semantic combination of morpho-orthographic segments).

As the authors mentioned themselves, their list of effects is not exhaustive. However, one would like to know whether, and if so, what role further variables such as surface frequency, word length, word class, abstractness, or cues to morpheme boundaries are supposed to play in word identification (e.g., Caramazza and Hillis, 1991; Inhoff et al., 2000; Taft, 2004; Baayen et al., 2007; Juhasz, 2008; Kuperman et al., 2009; Juhasz and Pollatsek, 2011; Hyönä, 2012). The impact of Amenta and Crepaldi's (2012) target list of relevant effects on future experimentation and theory development will depend on the relative contribution of all these variables. Consequently, one needs to discuss whether the additional variables mentioned here affect only later reading stages or what their role could be during word identification.

More generally, the authors seem to aim for a psycholinguistic, i.e., cognitive model of language behavior rather than a linguistic theory. (This is not the same; morphological effects can, for example, be simulated without an implementation of morphology; cf. Baayen et al., 2011.) They also refer to some eye-tracking and electrophysiological studies which provide neural evidence. It remains unclear how the neural evidence is to be incorporated into a strictly cognitive model. Alternatively, one may aim for a neuro-cognitive model of language behavior and visual word identification in particular. If one is to construct a complete model of such a phenomenon, the effects (behavior) but also the causes (neural activity) appear to be relevant and should be considered. While Amemta and Crepaldi's (2012) work to consider the large body of behavioral evidence is certainly ambitious, future work should take neural evidence also into account because cognitive and neural evidence can be mutually informative and helpful in understanding language performance (Grimaldi, 2012).

Another methodological issue arises from the suggestion that models of visual word identification should explain the effects listed by Amenta and Crepaldi (2012) because the list comprises aspects of experimental techniques (masking). Masking does not pertain to the phenomenon in question but experimental paradigms can be modeled. For example, Norris and Kinoshita (2008) proposed that in masked priming, prime, and target are perceptually fused into a single percept or object. As a consequence, masked priming effects may depend on the task requirements rather than on the relation between prime and target representations. Norris and Kinoshita (2008) show that priming effects can be shifted from word stimuli to non-word stimuli by using a same-different task rather than a lexical decision task. For the present discussion, one can doubt whether psycholinguistic models of word identification have to combine methodological aspects such as masked priming with the processes of interest, i.e., visual word identification.

Although some questions remain, Amenta and Crepaldi's (2012) review is bound to stimulate scientific discussions regarding visual word identification and provoke further research efforts to better understand morphological processing. Next steps of enquiry may focus on the different domains of morphology, inflection, derivation, or compounding drawing on as many sources of evidence as possible (e.g., different populations, methodologies, or language families) to comprehensively describe each domain (cf. Niemi et al., 1994; Marslen-Wilson and Tyler, 2007). Another major challenge is the theoretical unification of different sensory modalities. Finally, future reviews would be highly informative, if they use quantitative evaluations of research findings. One might perform meta-analyses or quantify the frequency of replications of particular effects. This way, our understanding of the connection between morphology and how it is represented and controlled by the human brain may be fostered (cf. Grimaldi, 2012).

# **References**

*Received: 15 July 2012; accepted: 21 September 2012; published online: 09 October 2012.*

*Citation: Koester D (2012) Future morphology? Summary of visual word identification effects draws attention to necessary efforts in understanding morphological processing. Front. Psychology 3:395. doi: 10.3389/fpsyg.2012.00395 This article was submitted to Frontiers in Language Sciences, a specialty of Frontiers in Psychology.*

*Copyright © 2012 Koester. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.*