Play-Persona: Modeling Player Behaviour in Computer Games

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Compendium Articles 67
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Play-Persona: Modeling Player Behav
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4.2.3 PLAYER EXPRESSION 53 4.3 PEIR
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ACKNOWLEDGEMENTS I would like to th
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ABSTRACT This thesis proposes a fra
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Analysis seeks to break down comple
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Considering games as acts of commun
Page 16 and 17: ensemble - the syntagm. Selection r
Page 18 and 19: described in terms of likelihood of
Page 20 and 21: ook: FLUX, published in 2008. This
Page 23 and 24: 2. A THEORETICAL FRAMEWORK FOR MODE
Page 25 and 26: “positivism” and “anti-positi
Page 27 and 28: with the game world based solely on
Page 29 and 30: on that text. Later, Barthes introd
Page 31 and 32: according to their mental state. Wh
Page 33: Following the humanistic tradition
Page 36 and 37: In most games it is not realistic t
Page 38 and 39: ehaviour. Assessing assumptions on
Page 40 and 41: Independently on the identity of ea
Page 42 and 43: main focus of the brand has always
Page 44 and 45: 3.4 Play-persona as a lens (a poste
Page 46 and 47: Figure 10: personality and subperso
Page 49 and 50: 4. CREATING EXPERIENCES The MDA fra
Page 51 and 52: Play-styles are sets of isotopic ga
Page 53 and 54: 4.2.3 Player expression Player expr
Page 55: Players will eventually understand
Page 58 and 59: Combining both of these two approac
Page 61 and 62: REFERENCES 1. Aarseth, E. (1997): C
Page 63 and 64: 52. Hunicke, R. LeBlanc, M. & Zubek
Page 65: 96. Wiggins, A. (2006): Data Driven
Page 70 and 71: patterns are of an emotional, ratio
Page 72 and 73: FIGURE 1 Perceptive and Reflective
Page 74 and 75: Candace Pert is the neuroscientist
Page 76 and 77: esearch that dealt with categorizin
Page 78 and 79: 4.2 PSYCHOSYNTHESIS Psychosynthesis
Page 80 and 81: emergence of subpersonalities, it i
Page 82 and 83: conception of a game”. These diff
Page 84 and 85: Following up the “soft edges” m
Page 86 and 87: impossible to continue pursuing the
Page 88 and 89: in terms of what kind of experience
Page 90 and 91: 33. THAGARD, P. & BARNES, A. Emotio
Page 92 and 93: 1 INTRODUCTION Fig. 1 Player, actio
Page 94 and 95: 3 AESTHETIC ANALYSIS 3.1 Histograms
Page 96 and 97: 4 ARCHITECTURAL ANALYSIS 4.1 Macro
Page 98 and 99: threshold. For the “Loading” ar
Page 100 and 101: 6 PHENOMENOLOGICAL ANALYSIS 6.1 Pla
Page 102 and 103: lood pressure. Through their patent
Page 104 and 105: REFERENCES 1. Carson, D. Environmen
Page 106 and 107: As a possible way out from this imp
Page 108 and 109: perfect in order to obtain an in-de
Page 110 and 111: Lindley defines play-gestalts as pa
Page 112 and 113: schema) are nearly identical, but t
Page 114 and 115: 6 PLAY-PERSONAS When a player behav
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116
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2 Computer games and game metrics C
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Designers can use personas as categ
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Play or blending in; it consists of
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7 Conclusions The play-persona fram
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early example of Deus Ex, a First-P
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designed to a specific subject area
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interact with objects and entities
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Table 1: A model of the full range
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Figure 3: (left): Tracking the move
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Appearance Hitman: Blood Money feat
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the requirements of the analysis in
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5. USING PERSONAS: “Hitman: Blood
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Interaction possibility field: Ther
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generated content and flow maintena
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146
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way to express themselves. For exam
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foreshadowing of a reader competent
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3.1 Gameplay metrics Gameplay metri
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of discovering patterns in the usag
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4.1 Grunt persona Narrative descrip
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Fig. 5: Personas as lenses show the
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type of games would not particularl
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17. Ravaja, N., Saari, T., Laarni,
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increasing complexity of contempora
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of user behavior via gameplay metri
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In terms of using gameplay metrics
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custom in house-produced tool which
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In the map level selected for this
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gameplay metrics cannot inform abou
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Figure 4: Screenshot from Kane & Ly
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Figure 5: The path of the tester ha
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use of game metrics for gameplay an
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19. Jørgensen, A. H.: Marrying HCI
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184
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The methods currently employed for
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is a computerized data management s
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fair degree of precision, e.g. repo
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only rarely done. One of the few pu
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which can subsequently be analyzed
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3. TOMB RAIDER: A CASE STUDY To sho
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Figure 4: Grid-based heatmap of the
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Figure 7: Detail of the overlay ana
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user research experts in game devel
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19. Korhonen, H., Koivisto, E.M.I.:
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206
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Within the last five years, instrum
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ehaviors in Quake II while White an
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Note that at the moment of writing
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6) Help-on-Demand: The number of ti
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7. RESULTS This section presents th
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7.2 ESOM Fig. 4. Rectangular lattic
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areas represent relatively large va
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(a) Cause of Death: Opponent, Do (b
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8. DISCUSSION & CONCLUSIONS This pa
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REFERENCES 1. K. Isbister and N. Sc
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228
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fear”). The German terms “erfah
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There are only few ways to move and
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game variables should be monitored
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References 1. Bartle R.A. Player wh
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23. Nijholt, A., BCI for Games: A
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1. INTRODUCTION In the past, user-c
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3. POINTS OF INTERACTIVITY Each pan
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14. L. Nacke, C. Lindley, and S. St
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interaction options between the use
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Gameplay metrics form a novel appro
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250
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greater degree, as is evidenced by
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cognitive imperative, an innate nee
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3.1 From personas to Play-personas
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gameplay metrics data gathered from
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considered to be pre-linguistic and
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enemies, the number of times a weap
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million. Every time players ask for
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In a quantitative approach towards
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preliminary hypothesis of in-game b
Page 270:
40. Davis, J.; Steury, K., Pagulaya
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Play-Persona: Modeling Player Behaviour in Computer Games

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